Conventional and advanced magnetic resonance imaging in patients with high-grade glioma

Innovation in Non-Invasive Diagnosis and Disease Monitoring for Meningiomas

Meningiomas are tumors of the central nervous system that vary in their presentation, ranging from benign and slow-growing to highly aggressive. The standard method for diagnosing and classifying meningiomas involves invasive surgery and can fail to provide accurate prognostic information. Liquid biopsy methods, which exploit circulating tumor biomarkers such as DNA, extracellular vesicles, micro-RNA, proteins, and more, offer a non-invasive and dynamic approach for tumor classification, prognostication, and evaluating treatment response. Currently, a clinically approved liquid biopsy test for meningiomas does not exist. This review provides a discussion of current research and the challenges of implementing liquid biopsy techniques for advancing meningioma patient care.

A Biopsy-Controlled Prospective Study of Contrast-Enhancing Diffuse Glioma Infiltration Based on FET-PET and FLAIR

Accurately defining glioma infiltration is crucial for optimizing radiotherapy and surgery, but glioma infiltration is heterogeneous and MRI imperfectly defines the tumor extent. Currently, it is impossible to determine the tumor infiltration gradient within a FLAIR signal. O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-PET often reveals high-grade glioma infiltration beyond contrast-enhancing areas on MRI. Here, we studied FET uptake dynamics in tumor and normal brain structures by dual-timepoint (10 min and 40-60 min post-injection) acquisition to optimize analysis protocols for defining glioma infiltration. Over 300 serial stereotactic biopsies from 23 patients (mean age 47, 12 female/11 male) of diffuse contrast-enhancing gliomas were taken from areas inside and outside contrast enhancement or outside the FET hotspot but inside FLAIR. The final diagnosis was G4 in 11, grade 3 in 10, and grade 2 in 2 patients. The target-to-background (TBRs) ratios and standardized uptake values (SUVs) were calculated in areas used for biopsy planning and in background structures. The optimal method and threshold values were determined to find a preferred strategy for defining glioma infiltration. Standard thresholding (1.6× uptake in the contralateral brain) in standard acquisition PET images differentiated a tumor of any grade from astrogliosis, although the uptake in astrogliosis and grade 2 glioma was similar. Analyzing an optimal strategy for infiltration volume definition astrogliosis could be accurately differentiated from tumor samples using a choroid plexus as a background. Early acquisition improved the AUC in many cases, especially within FLAIR, from 56% to 90% sensitivity and 41% to 61% specificity (standard TBR 1.6 vs. early TBR plexus). The current FET-PET evaluation protocols for contrast-enhancing gliomas are limited, especially at the tumor border where grade 2 tumor and astrogliosis have similar uptake, but using choroid plexus uptake in early acquisitions as a background, we can precisely define a tumor within FLAIR that was outside of the scope of current FET-PET protocols.

Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series

Introduction and importance

Automated segmentation of glioblastoma multiforme (GBM) from MRI images is crucial for accurate diagnosis and treatment planning. This paper presents a new and innovative approach for automating the segmentation of GBM from MRI images using the marker-controlled watershed segmentation (MCWS) algorithm.

Case presentation and methods

The technique involves several image processing techniques, including adaptive thresholding, morphological filtering, gradient magnitude calculation, and regional maxima identification. The MCWS algorithm efficiently segments images based on local intensity structures using the watershed transform, and fuzzy c-means (FCM) clustering improves segmentation accuracy. The presented approach achieved improved segmentation accuracy in detecting and segmenting GBM tumours from axial T2-weighted (T2-w) MRI images, as demonstrated by the mean characteristics performance metrics for GBM segmentation (sensitivity: 0.9905, specificity: 0.9483, accuracy: 0.9508, precision: 0.5481, F_measure: 0.7052, and jaccard: 0.9340).

Clinical discussion

The results of this study underline the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumours.

Conclusion

The MCWS technique provides an effective and efficient approach for the segmentation of challenging medical images.

Explainable hybrid vision transformers and convolutional network for multimodal glioma segmentation in brain MRI

Accurate localization of gliomas, the most common malignant primary brain cancer, and its different sub-region from multimodal magnetic resonance imaging (MRI) volumes are highly important for interventional procedures. Recently, deep learning models have been applied widely to assist automatic lesion segmentation tasks for neurosurgical interventions. However, these models are often complex and represented as "black box" models which limit their applicability in clinical practice. This article introduces new hybrid vision Transformers and convolutional neural networks for accurate and robust glioma segmentation in Brain MRI scans. Our proposed method, TransXAI, provides surgeon-understandable heatmaps to make the neural networks transparent. TransXAI employs a post-hoc explanation technique that provides visual interpretation after the brain tumor localization is made without any network architecture modifications or accuracy tradeoffs. Our experimental findings showed that TransXAI achieves competitive performance in extracting both local and global contexts in addition to generating explainable saliency maps to help understand the prediction of the deep network. Further, visualization maps are obtained to realize the flow of information in the internal layers of the encoder-decoder network and understand the contribution of MRI modalities in the final prediction. The explainability process could provide medical professionals with additional information about the tumor segmentation results and therefore aid in understanding how the deep learning model is capable of processing MRI data successfully. Thus, it enables the physicians' trust in such deep learning systems towards applying them clinically. To facilitate TransXAI model development and results reproducibility, we will share the source code and the pre-trained models after acceptance at https://github.com/razeineldin/TransXAI .

Diagnostic Accuracy of MR Spectroscopic Imaging and 18F-FET PET for Identifying Glioma: A Biopsy-Controlled Hybrid PET/MRI Study

Contrast-enhanced MRI is the method of choice for brain tumor diagnostics, despite its low specificity for tumor tissue. This study compared the contribution of MR spectroscopic imaging (MRSI) and amino acid PET to improve the detection of tumor tissue. Methods: In 30 untreated patients with suspected glioma, O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) PET; 3-T MRSI with a short echo time; and fluid-attenuated inversion recovery, T2-weighted, and contrast-enhanced T1-weighted MRI were performed for stereotactic biopsy planning. Serial samples were taken along the needle trajectory, and their masks were projected to the preoperative imaging data. Each sample was individually evaluated neuropathologically. 18F-FET uptake and the MRSI signals choline (Cho), N-acetyl-aspartate (NAA), creatine, myoinositol, and derived ratios were evaluated for each sample and classified using logistic regression. The diagnostic accuracy was evaluated by receiver operating characteristic analysis. Results: On the basis of the neuropathologic evaluation of tissue from 88 stereotactic biopsies, supplemented with 18F-FET PET and MRSI metrics from 20 areas on the healthy-appearing contralateral hemisphere to balance the glioma/nonglioma groups, 18F-FET PET identified glioma with the highest accuracy (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.81-0.93; threshold, 1.4 × background uptake). Among the MR spectroscopic metabolites, Cho/NAA normalized to normal brain tissue showed the highest diagnostic accuracy (area under the receiver operating characteristic curve, 0.81; 95% CI, 0.71-0.88; threshold, 2.2). The combination of 18F-FET PET and normalized Cho/NAA did not improve the diagnostic performance. Conclusion: MRI-based delineation of gliomas should preferably be supplemented by 18F-FET PET.

The MANGO study: a prospective investigation of oxygen enhanced and blood-oxygen level dependent MRI as imaging biomarkers of hypoxia in glioblastoma

Background

Glioblastoma (GBM) is the most aggressive type of brain cancer, with a 5-year survival rate of ~5% and most tumours recurring locally within months of first-line treatment. Hypoxia is associated with worse clinical outcomes in GBM, as it leads to localized resistance to radiotherapy and subsequent tumour recurrence. Current standard of care treatment does not account for tumour hypoxia, due to the challenges of mapping tumour hypoxia in routine clinical practice. In this clinical study, we aim to investigate the role of oxygen enhanced (OE) and blood-oxygen level dependent (BOLD) MRI as non-invasive imaging biomarkers of hypoxia in GBM, and to evaluate their potential role in dose-painting radiotherapy planning and treatment response assessment.

Methods

The primary endpoint is to evaluate the quantitative and spatial correlation between OE and BOLD MRI measurements and [18F]MISO values of uptake in the tumour. The secondary endpoints are to evaluate the repeatability of MRI biomarkers of hypoxia in a test-retest study, to estimate the potential clinical benefits of using MRI biomarkers of hypoxia to guide dose-painting radiotherapy, and to evaluate the ability of MRI biomarkers of hypoxia to assess treatment response. Twenty newly diagnosed GBM patients will be enrolled in this study. Patients will undergo standard of care treatment while receiving additional OE/BOLD MRI and [18F]MISO PET scans at several timepoints during treatment. The ability of OE/BOLD MRI to map hypoxic tumour regions will be evaluated by assessing spatial and quantitative correlations with areas of hypoxic tumour identified via [18F]MISO PET imaging.

Discussion

MANGO (Magnetic resonance imaging of hypoxia for radiation treatment guidance in glioblastoma multiforme) is a diagnostic/prognostic study investigating the role of imaging biomarkers of hypoxia in GBM management. The study will generate a large amount of longitudinal multimodal MRI and PET imaging data that could be used to unveil dynamic changes in tumour physiology that currently limit treatment efficacy, thereby providing a means to develop more effective and personalised treatments.

Association of MRI Volume Parameters in Predicting Patient Outcome at Time of Initial Diagnosis of Glioblastoma

PURPOSE

Patients with glioblastoma (GBM) may demonstrate varying patterns of infiltration and relapse. Improving the ability to predict these patterns may influence the management strategies at the time of initial diagnosis. This study aims to examine the impact of the ratio (T2/T1) of the non-enhancing volume in T2-weighted images (T2) to the enhancing volume in MRI T1-weighted gadolinium-enhanced images (T1gad) on patient outcome.

METHODS AND MATERIALS

A retrospective audit was performed from established prospective databases in patients managed consecutively with radiation therapy (RT) for GBM between 2016 and 2019. Patient, tumour and treatment-related factors were assessed in relation to outcome. Volumetric data from the initial diagnostic MRI were obtained via the manual segmentation of the T1gd and T2 abnormalities. A T2/T1 ratio was calculated from these volumes. The initial relapse site was assessed on MRI in relation to the site of the original T1gad volume and surgical cavity. The major endpoints were median relapse-free survival (RFS) from the date of diagnosis and site of initial relapse (defined as either local at the initial surgical site or any distance more than 20 mm from initial T1gad abnormality). The analysis was performed for association between known prognostic factors as well as the radiological factors using log-rank tests for subgroup comparisons, with correction for multiple comparisons.

RESULTS

One hundred and seventy-seven patients with GBM were managed consecutively with RT between 2016 and 2019 and were eligible for the analysis. The median age was 62 years. Seventy-four percent were managed under a 60Gy (Stupp) protocol, whilst 26% were on a 40Gy (Elderly) protocol. Major neuroanatomical subsites were Lateral Temporal (18%), Anterior Temporal (13%) and Medial Frontal (10%). Median volumes on T1gd and T2 were 20 cm3 (q1-3:8-43) and 37 cm3 (q1-3: 17-70), respectively. The median T2/T1 ratio was 2.1. For the whole cohort, the median OS was 16.0 months (95%CI:14.1-18.0). One hundred and forty-eight patients have relapsed with a median RFS of 11.4 months (95%CI:10.4-12.5). A component of distant relapse was evident in 43.9% of relapses, with 23.6% isolated relapse. Better ECOG performance Status (p = 0.007), greater extent of resection (p = 0.020), MGMT methylation (p < 0.001) and RT60Gy Dose (p = 0.050) were associated with improved RFS. Although the continuous variable of initial T1gd volume (p = 0.39) and T2 volume (p = 0.23) were not associated with RFS, the lowest T2/T1 quartile (reflecting a relatively lower T2 volume compared to T1gd volume) was significantly associated with improved RFS (p = 0.016) compared with the highest quartile. The lowest T2/T1 ratio quartile was also associated with a lower risk of distant relapse (p = 0.031).

CONCLUSION

In patients diagnosed with GBM, the volumetric parameters of the diagnostic MRI with a ratio of T2 and T1gad abnormality may assist in the prediction of relapse-free survival and patterns of relapse. A further understanding of these relationships has the potential to impact the design of future radiation therapy target volume delineation protocols.

Tumefactive demyelinating lesions versus CNS neoplasms, a comparative study

BACKGROUND

Differentiating tumefactive demyelinating lesions (TDL) from neoplasms of the central nervous system continues to be a diagnostic dilemma in many cases.

OBJECTIVE

Our study aimed to examine and contrast the clinical and radiological characteristics of TDL, high-grade gliomas (HGG) and primary CNS lymphoma (CNSL).

METHOD

This was a retrospective review of 66 patients (23 TDL, 31 HGG and 12 CNSL). Clinical and laboratory data were obtained. MRI brain at presentation were analyzed by two independent, blinded neuroradiologists.

RESULTS

Patients with TDLs were younger and predominantly female. Sensorimotor deficits and ataxia were more common amongst TDL whereas headaches and altered mental status were associated with HGG and CNSL. Compared to HGG and CNSL, MRI characteristics supporting TDL included relatively smaller size, lack of or mild mass effect, incomplete peripheral rim enhancement, absence of central enhancement or restricted diffusion, lack of cortical involvement, and presence of remote white matter lesions on the index scan. Paradoxically, some TDLs may present atypically or radiologically mimic CNS lymphomas.

CONCLUSION

Careful evaluation of clinical and radiological features helps in differentiating TDLs at first presentation from CNS neoplasms.

MRI and amino acid PET detection of whole-brain tumor burden

Background

[18F]fluciclovine amino acid PET has shown promise for detecting brain tumor regions undetected on conventional anatomic MRI scans. However, it remains unclear which of these modalities provides a better assessment of the whole brain tumor burden. This study quantifies the performance of [18F]fluciclovine PET and MRI for detecting the whole brain tumor burden.

Methods

Thirteen rats were orthotopically implanted with fluorescently transduced human glioblastoma cells. Rats underwent MRI (T1- and T2-weighted) and [18F]fluciclovine PET. Next brains were excised, optically cleared, and scanned ex vivo with fluorescence imaging. All images were co-registered using a novel landmark-based registration to enable a spatial comparison. The tumor burden identified on the fluorescent images was considered the ground truth for comparison with the in vivo imaging.

Results

Across all cases, the PET sensitivity for detecting tumor burden (median 0.67) was not significantly different than MRI (combined T1+T2-weighted) sensitivity (median 0.61; p=0.85). However, the combined PET+MRI sensitivity (median 0.86) was significantly higher than MRI alone (41% higher; p=0.004) or PET alone (28% higher; p=0.0002). The specificity of combined PET+MRI (median=0.91) was significantly lower compared with MRI alone (6% lower; p=0.004) or PET alone (2% lower; p=0.002).

Conclusion

In these glioblastoma xenografts, [18F]fluciclovine PET did not provide a significant increase in tumor burden detection relative to conventional anatomic MRI. However, a combined PET and MRI assessment did significantly improve detection sensitivity relative to either modality alone, suggesting potential value in a combined assessment for some tumors.

In vivo magnetic resonance spectroscopy for the differential diagnosis of a cerebral mass in a boy with precocious puberty: a case report and review of the literature

PURPOSE

To highlight the role of in vivo magnetic resonance spectroscopy (MRS) as a non-invasive tool that can clarify the etiology of sellar tumors by presenting the case of a boy with central precocious puberty (CPP) and to review the current literature.

METHODS

A 4-year-old boy was admitted to our hospital due to repeated episodes of focal and gelastic seizures in the previous year. Clinical examination (testicular volume 4-5 ml bilaterally, penile length of 7.5 cm, and absence of axillary or pubic hair) and laboratory tests (FSH, LH, and testosterone) were indicative of CPP. The combination of gelastic seizures with CPP in a 4-year-old boy raised the suspicion of hypothalamic hamartoma (HH). Brain MRI revealed a lobular mass in the suprasellar-hypothalamic region. The differential diagnosis included glioma, HH, and craniopharyngioma. To further investigate the CNS mass, an in vivo brain MRS was performed.

RESULTS

Οn conventional MRI, the mass demonstrated isointensity to gray matter on T1 weighted images but slight hyperintensity on T2-weighted images. It did not show restricted diffusion or contrast enhancement. On MRS, it showed reduced N-acetyl aspartate (NAA) and slightly elevated myoinositol (MI) compared with values in normal deep gray matter. The MRS spectrum, in combination with the conventional MRI findings, were consistent with the diagnosis of a HH.

CONCLUSION

MRS is a state-of-the-art, non-invasive imaging technique that compares the chemical composition of normal tissue to that of abnormal regions by juxtaposing the frequency of measured metabolites. MRS, in combination with clinical evaluation and classic MRI, can provide identification of CNS masses, thus eliminating the need for an invasive biopsy.

Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma

BACKGROUND

Differentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients.

OBJECTIVE

Based on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma.

METHODS

Patients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient's Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed.

RESULTS

Brain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively.

CONCLUSION

Combining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.

Advances in the Radiological Evaluation of and Theranostics for Glioblastoma

Imaging is essential for evaluating patients with glioblastoma. Traditionally a multimodality undertaking, CT, including CT cerebral blood profusion, PET/CT with traditional fluorine-18 fluorodeoxyglucose (18F-FDG), and MRI have been the mainstays for diagnosis and post-therapeutic assessment. However, recent advances in these modalities, in league with the emerging fields of radiomics and theranostics, may prove helpful in improving diagnostic accuracy and treating the disease.

Long-Acting Therapeutic Delivery Systems for the Treatment of Gliomas

Despite the emergence of cutting-edge therapeutic strategies and tremendous progress in research, a complete cure of glioma remains elusive. The heterogenous nature of tumor, immunosuppressive state and presence of blood brain barrier are few of the major obstacles in this regard. Long-acting depot formulations such as injectables and implantables are gaining attention for drug delivery to brain owing to their ease in administration and ability to elute drug locally for extended durations in a controlled manner with minimal toxicity. Hybrid matrices fabricated by incorporating nanoparticulates within such systems help to enhance pharmaceutical advantages. Utilization of long-acting depots as monotherapy or in conjunction with existing strategies rendered significant survival benefits in many preclinical studies and some clinical trials. The discovery of novel targets, immunotherapeutic strategies and alternative drug administration routes are now coupled with several long-acting systems with an ultimate aim to enhance patient survival and prevent glioma recurrences.

Amino Acid PET in Neurooncology

For decades, several amino acid PET tracers have been used to optimize diagnostics in patients with brain tumors. In clinical routine, the most important clinical indications for amino acid PET in brain tumor patients are differentiation of neoplasm from nonneoplastic etiologies, delineation of tumor extent for further diagnostic and treatment planning (i.e., diagnostic biopsy, resection, or radiotherapy), differentiation of treatment-related changes such as pseudoprogression or radiation necrosis after radiation or chemoradiation from tumor progression at follow-up, and assessment of response to anticancer therapy, including prediction of patient outcome. This continuing education article addresses the diagnostic value of amino acid PET for patients with either glioblastoma or metastatic brain cancer.

F18-FET PET in pediatric brain tumors: integrative analysis of image derived parameters and clinico-pathological data

BACKGROUND

F18-FET PET has an established diagnostic role in adult brain gliomas. In this study we analyzed image derived static and dynamic parameters with available conventional MRI, histological, clinical and follow-up data in assessment of pediatric brain tumor patients at different stages of the disease.

METHODS

Forty-four pediatric patients with median age 7 years, diagnosed with brain tumors and underwent forty-seven 18F-FET PET scans either initially (20 scans) or post-therapy (27 scans) were enrolled. Standardized analysis of summed FET PET images early from 10-20 min and late from 30-40 min post-injection were used for static (mean and maximum tumor to brain ratio [TBR] and biological tumor volume [BTV]) parameters evaluation as well as the time activity curve [TAC].

RESULTS

Nineteen out of 20 initially assessed patients had pathologically and/or clinico-radiologically proven neoplastic lesions and one patient had pathologically proven abscess. Receiver operator curve (ROC) marked early TBR max 2.95, early TBR mean 1.76, late TBR max 2.5 and late TBR mean 1.74 as discriminator points with diagnostic accuracy reaching 90% when TBR max was combined with dynamic parameters. Significant association was found between initial FET scans, early and late BTV and event free survival (EFS) (P value=0.042 and 0.005 respectively). In post-therapy assessment, the diagnostic accuracy of conventional MRI was 81.48% when used alone and 96.30% when combined with F18-FET PET scan findings. A cutoff point of 3.2 cm³ for late BTV, in post-therapy scans, was successfully marked as a predictor for therapy response (P value 0.042) and was significantly associated with EFS (P value 0.002). In FET-avid / MRI non-enhancing lesions, early TBR max was able to detect highly malignant processes (high-grade tumors in initial scans and residue/recurrence in post-therapy scans) with 80% sensitivity and 100% specificity when cutoff value of 2.25 was used (P value=0.024). In patients with FET-avid brainstem lesions, whether enhancing or non-enhancing in MRI scans, 81.8% were associated with high risk diagnoses and 68.2% of them were associated with poor therapy outcome. The degree of FET uptake matched tumor-grading, but did not show significant association with OS or EFS (P value>0.05).

CONCLUSIONS

F18-FET PET seems to be an evolving pediatric neuro-imaging technique with valuable diagnostic and prognostic information at initial and post-therapy evaluation.

Glioma radiogenomics and artificial intelligence: road to precision cancer medicine

Radiogenomics refers to the study of the relationship between imaging phenotypes and gene expression patterns/molecular characteristics, which might allow improved diagnosis, decision-making, and predicting patient outcomes in the context of multiple diseases. Central nervous system (CNS) tumours contribute to significant cancer-related mortality in the present age. Although historically CNS neoplasms were classified and graded based on microscopic appearance, there was discordance between two histologically similar tumours that showed varying prognosis and behaviour, attributable to their molecular signatures. These led to the incorporation of molecular markers in the classification of CNS neoplasms. Meanwhile, advancements in imaging technology such as diffusion-based imaging (including tractography), perfusion, and spectroscopy in addition to the conventional imaging of glial neoplasms, have opened an avenue for radiogenomics. This review touches upon the schema of the current classification of gliomas, concepts behind molecular markers, and parameters that are used in radiogenomics to characterise gliomas and the role of artificial intelligence for the same. Further, the role of radiomics in the grading of brain tumours, prediction of treatment response and prognosis has been discussed. Use of automated and semi-automated tumour segmentation for radiotherapy planning and follow-up has also been discussed briefly.

MMP-9 as Prognostic Marker for Brain Tumours: A Comparative Study on Serum-Derived Small Extracellular Vesicles

Matrix metalloproteinase-9 (MMP-9) degrades the extracellular matrix, contributes to tumour cell invasion and metastasis, and its elevated level in brain tumour tissues indicates poor prognosis. High-risk tissue biopsy can be replaced by liquid biopsy; however, the blood-brain barrier (BBB) prevents tumour-associated components from entering the peripheral blood, making the development of blood-based biomarkers challenging. Therefore, we examined the MMP-9 content of small extracellular vesicles (sEVs)-which can cross the BBB and are stable in body fluids-to characterise tumours with different invasion capacity. From four patient groups (glioblastoma multiforme, brain metastases of lung cancer, meningioma, and lumbar disc herniation as controls), 222 serum-derived sEV samples were evaluated. After isolating and characterising sEVs, their MMP-9 content was measured by ELISA and assessed statistically (correlation, paired t-test, Welch's test, ANOVA, ROC). We found that the MMP-9 content of sEVs is independent of gender and age, but is affected by surgical intervention, treatment, and recurrence. We found a relation between low MMP-9 level in sEVs (<28 ppm) and improved survival (8-month advantage) of glioblastoma patients, and MMP-9 levels showed a positive correlation with aggressiveness. These findings suggest that vesicular MMP-9 level might be a useful prognostic marker for brain tumours.

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.

Liquid Biopsy in Glioblastoma

Simple Summary

Glioblastoma is the most common and malignant primary brain tumor. Despite intensive research for new treatments, the survival of patients has not significantly improved in recent decades. Currently, glioblastoma is mainly diagnosed by neuroimaging techniques followed by histopathological and molecular analysis of the resected or biopsied tissue. Both imaging and tissue-based methods have, despite their advantages, some important limitations highlighting the necessity for alternative techniques such as liquid biopsy. It appears as an attractive and non-invasive alternative to support the diagnosis and the follow-up of patients with glioblastoma and to identify early recurrence. Liquid biopsy, primarily through blood tests, involves the detection and quantification of tumoral content released by tumors into the biofluids. The aim of the present review is to discuss the biological bases, the advantages, and the disadvantages of the most important circulating biomarkers so far proposed for glioblastoma.

Abstract

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Despite recent advances in therapy modalities, the overall survival of GBM patients remains poor. GBM diagnosis relies on neuroimaging techniques. However, confirmation via histopathological and molecular analysis is necessary. Given the intrinsic limitations of such techniques, liquid biopsy (mainly via blood samples) emerged as a non-invasive and easy-to-implement alternative that could aid in both the diagnosis and the follow-up of GBM patients. Cancer cells release tumoral content into the bloodstream, such as circulating tumor DNA, circulating microRNAs, circulating tumor cells, extracellular vesicles, or circulating nucleosomes: all these could serve as a marker of GBM. In this narrative review, we discuss the current knowledge, the advantages, and the disadvantages of each circulating biomarker so far proposed.

Repeatability of radiotherapy dose-painting prescriptions derived from a multiparametric magnetic resonance imaging model of glioblastoma infiltration

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Magnetic resonance imaging was used to derive dose-painting prescriptions in glioma.

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Dose prescriptions derived from magnetic resonance imaging are highly repeatable.

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Dose-painting plans are more repeatable than their dose prescriptions.

Background and purpose

Glioblastoma (GBM) patients have a dismal prognosis. Tumours typically recur within months of surgical resection and post-operative chemoradiation. Multiparametric magnetic resonance imaging (mpMRI) biomarkers promise to improve GBM outcomes by identifying likely regions of infiltrative tumour in tumour probability (TP) maps. These regions could be treated with escalated dose via dose-painting radiotherapy to achieve higher rates of tumour control. Crucial to the technical validation of dose-painting using imaging biomarkers is the repeatability of the derived dose prescriptions. Here, we quantify repeatability of dose-painting prescriptions derived from mpMRI.

Materials and methods

TP maps were calculated with a clinically validated model that linearly combined apparent diffusion coefficient (ADC) and relative cerebral blood volume (rBV) or ADC and relative cerebral blood flow (rBF) data. Maps were developed for 11 GBM patients who received two mpMRI scans separated by a short interval prior to chemoradiation treatment. A linear dose mapping function was applied to obtain dose-painting prescription (DP) maps for each session. Voxel-wise and group-wise repeatability metrics were calculated for parametric, TP and DP maps within radiotherapy margins.

Results

DP maps derived from mpMRI were repeatable between imaging sessions (ICC > 0.85). ADC maps showed higher repeatability than rBV and rBF maps (Wilcoxon test, p = 0.001). TP maps obtained from the combination of ADC and rBF were the most stable (median ICC: 0.89).

Conclusions

Dose-painting prescriptions derived from a mpMRI model of tumour infiltration have a good level of repeatability and can be used to generate reliable dose-painting plans for GBM patients.

Effect of 3D Slicer Preoperative Planning and Intraoperative Guidance with Mobile Phone Virtual Reality Technology on Brain Glioma Surgery

Objective

To explore the effect of 3D Slicer preoperative planning and intraoperative guidance with mobile phone virtual reality (VR) technology on brain glioma surgery.

Methods

By means of retrospective study, the data of 77 brain glioma patients treated in the neurosurgery departments at The Second Affiliated Hospital of Wannan Medical College and Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2015 to January 2022 were analyzed, and the patients were divided into the experimental group (EG, n = 38) and the control group (CG, n = 39) according to the surgical modalities. Before surgery, all patients received positron emission tomography-computed tomography (PET/CT) scanning and magnetic resonance imaging (MRI) examination. For patients in EG, the DICOM format images acquired from PET-CT and MRI examinations were imported with the 3D Slicer software for 3D visual fusion reconstruction, acquiring VR images, and developing detailed preoperative planning. Then, the reconstructed images were imported into the Sina software on a mobile phone, and the surgery was performed with the assistance of VR technology; for patients in CG, traditional 2D images were used for tumor contour drawing by the subjective visual method, and the craniotomy was performed under a traditional microscope. Patients' surgery indicators and Karnofsky Performance Scale (KPS) scores were compared between the two groups.

Results

The number of cases with total resection, rate of total resection, hospital stay after surgery, and surgery time were significantly better in EG than in CG (P < 0.05); after treatment, the KPS score was significantly higher in EG than in CG (75.66 ± 4.01 vs 65.36 ± 5.23, P < 0.001).

Conclusion

Combining 3D Slicer preoperative planning with intraoperative mobile phone VR technology can promote the accuracy of brain glioma surgery, which is conducive to effectively removing tumors while protecting patients' neural function.

What Does PET Imaging Bring to Neuro-Oncology in 2022? A Review

Simple Summary

Positron emission tomography (PET) imaging is increasingly used to supplement MRI in the management of patient with brain tumors. In this article, we provide a review of the current place and perspectives of PET imaging for the diagnosis and follow-up of from primary brain tumors such as gliomas, meningiomas and central nervous system lymphomas, as well as brain metastases. Different PET radiotracers targeting different biological processes are used to accurately depict these brain tumors and provide unique metabolic and biologic information. Radiolabeled amino acids such as [¹⁸F]FDOPA or [¹⁸F]FET are used for imaging of gliomas while both [¹⁸F]FDG and amino acids can be used for brain metastases. Meningiomas can be seen with a high contrast using radiolabeled ligands of somatostatin receptors, which they usually carry. Unconventional tracers that allow the study of other biological processes such as cell proliferation, hypoxia, or neo-angiogenesis are currently being studied for brain tumors imaging.

Abstract

PET imaging is being increasingly used to supplement MRI in the clinical management of brain tumors. The main radiotracers implemented in clinical practice include [¹⁸F]FDG, radiolabeled amino acids ([¹¹C]MET, [¹⁸F]FDOPA, [¹⁸F]FET) and [⁶⁸Ga]Ga-DOTA-SSTR, targeting glucose metabolism, L-amino-acid transport and somatostatin receptors expression, respectively. This review aims at addressing the current place and perspectives of brain PET imaging for patients who suffer from primary or secondary brain tumors, at diagnosis and during follow-up. A special focus is given to the following: radiolabeled amino acids PET imaging for tumor characterization and follow-up in gliomas; the role of amino acid PET and [¹⁸F]FDG PET for detecting brain metastases recurrence; [⁶⁸Ga]Ga-DOTA-SSTR PET for guiding treatment in meningioma and particularly before targeted radiotherapy.

Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation

Background: Pretreatment differentiation between glioblastoma and metastasis is a frequently encountered dilemma in neurosurgical practice. Distinction is required for precise planning of resection or radiotherapy, and also for defining further diagnostic procedures. Morphology and spectroscopy imaging features are not specific and frequently overlap. This limitation of magnetic resonance imaging and magnetic resonance spectroscopy was the reason to initiate this study. The aim of the present study was to determine whether the dataset of diffusion tensor imaging metrics contains information which may be used for the distinction between primary and secondary intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were measured in 81 patients with an expansive, ring-enhancing, intra-axial lesion on standard magnetic resonance imaging (1.5 T system). All tumors were histologically verified glioblastoma or secondary deposit. For qualitative analysis, two regions of interest were defined: intratumoral and immediate peritumoral region (locations 1 and 2, respectively). Fractional anisotropy and mean difusivity values of both groups were compared. Additional test was performed to determine if there was a significant difference in mean values between two locations. Results: A statistically significant difference was found in fractional anisotropy values among two locations, with decreasing values in the direction of neoplastic infiltration, although such difference was not observed in fractional anisotropy values in the group with secondary tumors. Mean difusivity values did not appear helpful in differentiation between these two entities. In both groups there was no significant difference in mean difusivity values, neither in intratumoral nor in peritumoral location. Conclusion: The results of our study justify associating the diffusion tensor imaging technique to conventional morphologic magnetic resonance imaging as an additional diagnostic tool for the distinction between primary and secondary intra-axial lesions. Quantitative analysis of diffusion tensor imaging metric, in particular measurement of fractional anisotropy in peritumoral edema facilitates accurate diagnosis.

Tuberculosis presenting with seizure and abdominal pain in a young female: A case report

In this case report, a 23-year-old female presented to the Emergency Department with complaints of abdominal pain, weight loss, progressive headaches, and an episode of seizure-like activity. Computerized tomography abdomen/pelvis revealed multilobulated ovarian masses and scattered peritoneal thickening. A brain Magnetic resonance imaging was ordered and demonstrated a peripherally enhancing intracranial mass. The brain lesion was resected and pathology revealed necrotizing granulomatous inflammation. Cultures were positive for acid fast bacilli. The patient was diagnosed with tuberculosis and treated with multidrug therapy. Upon further questioning, the patient had recently traveled to a tuberculosis endemic region. This case highlights the importance of an in-depth history and physical exam as a means to a more complete differential diagnosis considering the age of the patient and the findings on imaging.

Preliminary study of multiple b-value diffusion-weighted images and T1 post enhancement magnetic resonance imaging images fusion with Laplacian Re-decomposition (LRD) medical fusion algorithm for glioma grading

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LRD medical image fusion algorithm can be used for glioma grading.

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We can use the LRD fusion algorithm with MRI image for glioma grading.

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Fusing of DWI (b50) and T₁ enhancement (T₁Gd) by LRD, have highest diagnostic value for glioma grading.

Background

Grade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading.

Procedures

In this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves.

Findings

We found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images.

Conclusions

This research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.

Imaging Hallmarks of the Tumor Microenvironment in Glioblastoma Progression

Glioblastoma progression involves multifaceted changes in vascularity, cellularity, and metabolism. Capturing such complexities of the tumor niche, from the tumor core to the periphery, by magnetic resonance imaging (MRI) and spectroscopic imaging (MRSI) methods has translational impact. In human-derived glioblastoma models (U87, U251) we made simultaneous and longitudinal measurements of tumor perfusion (F_p), permeability (K^trans), and volume fractions of extracellular (v_e) and blood (v_p) spaces from dynamic contrast enhanced (DCE) MRI, cellularity from apparent diffusion coefficient (ADC) MRI, and extracellular pH (pH_e) from an MRSI method called Biosensor Imaging of Redundant Deviation in Shifts (BIRDS). Spatiotemporal patterns of these parameters during tumorigenesis were unique for each tumor. While U87 tumors grew faster, F_p, K^trans, and v_p increased with tumor growth in both tumors but these trends were more pronounced for U251 tumors. Perfused regions between tumor periphery and core with U87 tumors exhibited higher F_p, but K^trans of U251 tumors remained lowest at the tumor margin, suggesting primitive vascularization. Tumor growth was uncorrelated with v_e, ADC, and pH_e. U87 tumors showed correlated regions of reduced v_e and lower ADC (higher cellularity), suggesting ongoing proliferation. U251 tumors revealed that the tumor core had higher v_e and elevated ADC (lower cellularity), suggesting necrosis development. The entire tumor was uniformly acidic (pH_e 6.1-6.8) early and throughout progression, but U251 tumors were more acidic, suggesting lower aerobic glycolysis in U87 tumors. Characterizing these cancer hallmarks with DCE-MRI, ADC-MRI, and BIRDS-MRSI will be useful for exploring tumorigenesis as well as timely therapies targeted to specific vascular and metabolic aspects of the tumor microenvironment.

Recurrent Glioma: Does Qualitative Simultaneous 18F-DOPA PET/mp-MRI Improve Diagnostic Workup? An Initial Experience

RATIONALE OF THE STUDY

Neuroimaging modalities such as contrast-enhanced MRI and PET provide significant insight in the evaluation of gliomas. However, their reliability in successfully differentiating the tumor recurrence with treatment-related changes is still technologically challenging. The current study aims to qualitatively investigate the potential of the hybrid PET/multiparametric MRI modality to noninvasively distinguish between these 2 outcomes of brain tumor diagnostics for optimum and early patient management.

PATIENTS AND METHODS

A cohort of 26 suspected recurrent glioma cases proved on histology and/or clinicoradiological outcome forms the part of this study. A 3-point visual analytical scale was used to qualify lesions as recurrent or posttreatment radiation effects on PET, conventional MRI, dynamic susceptibility contrast-perfusion-weighted imaging, apparent diffusion coefficient, and the MR spectroscopy according to their level of suspicion.

RESULTS

Of the 26 patients, 21 patients were classified as recurrence and 5 as radiation necrosis. Advanced MRI parameters (perfusion, diffusion, and spectroscopy) integrated with 18F-DOPA PET imaging resulted in superior diagnostic performance obtained on visual assessment with an accuracy of 95%, sensitivity of 96%, and specificity approaching up to 100% over individual modalities.

CONCLUSIONS

The combination of multiple MR parameters evaluated together with 18F-DOPA PET offers an attractive approach to noninvasively distinguish true recurrence from radiation necrosis. However, further prospective studies with larger cohorts are warranted with additional neuropathological validations.

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Simple Summary

Radiogenomics enables prediction of the status and prognosis of patients using non-invasively obtained imaging data. Current machine learning (ML) methods used in radiogenomics require huge datasets, which involve the handling of large heterogeneous datasets from multiple cohorts/hospitals. In this study, two different glioma datasets were used to test various ML and image pre-processing methods to confirm whether the models trained on one dataset are universally applicable to other datasets. Our result suggested that the ML method that yielded the highest accuracy in a single dataset was likely to be overfitted. We demonstrated that implementation of standardization and dimension reduction procedures prior to classification, enabled the development of ML methods that are less affected by the multiple cohort difference. We advocate using caution in interpreting the results of radiogenomic studies of the training and testing datasets that are small or mixed, with a view to implementing practical ML methods in radiogenomics.

Abstract

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

Phase II trial of proton therapy versus photon IMRT for GBM: secondary analysis comparison of progression-free survival between RANO versus clinical assessment

Background

This secondary image analysis of a randomized trial of proton radiotherapy (PT) versus photon intensity-modulated radiotherapy (IMRT) compares tumor progression based on clinical radiological assessment versus Response Assessment in Neuro-Oncology (RANO).

Methods

Eligible patients were enrolled in the randomized trial and had MR imaging at baseline and follow-up beyond 12 weeks from completion of radiotherapy. “Clinical progression” was based on a clinical radiology report of progression and/or change in treatment for progression.

Results

Of 90 enrolled patients, 66 were evaluable. Median clinical progression-free survival (PFS) was 10.8 (range: 9.4–14.7) months; 10.8 months IMRT versus 11.2 months PT (P = .14). Median RANO-PFS was 8.2 (range: 6.9, 12): 8.9 months IMRT versus 6.6 months PT (P = .24). RANO-PFS was significantly shorter than clinical PFS overall (P = .001) and for both the IMRT (P = .01) and PT (P = .04) groups. There were 31 (46.3%) discrepant cases of which 17 had RANO progression more than a month prior to clinical progression, and 14 had progression by RANO but not clinical criteria.

Conclusions

Based on this secondary analysis of a trial of PT versus IMRT for glioblastoma, while no difference in PFS was noted relative to treatment technique, RANO criteria identified progression more often and earlier than clinical assessment. This highlights the disconnect between measures of tumor response in clinical trials versus clinical practice. With growing efforts to utilize real-world data and personalized treatment with timely adaptation, there is a growing need to improve the consistency of determining tumor progression within clinical trials and clinical practice.

Comparison of diagnostic value of 68 Ga-DOTATOC PET/MRI and standalone MRI for the detection of intracranial meningiomas

To evaluate the diagnostic performance of magnetic resonance imaging (MRI) alone in comparison to positron emission tomography/ magnetic resonance imaging (PET/MRI) in patients with meningiomas. 57 patients with a total of 112 meningiomas of the brain were included. PET/MRI, including a fully diagnostic contrast enhanced MRI and PET, were acquired. PET/MRI was used as reference standard. The size and location of meningiomas was recorded. Likelihood-ratio chi-square tests were used to calculate p-values within logistic regression in order to compare different models. A multi-level logistic regression was applied to comply the hierarchical data structure. Multi-level regression adjusts for clustering in data was performed. The majority (n = 103) of meningiomas could be identified based on standard MRI sequences compared to PET/MRI. MRI alone achieved a sensitivity of 95% (95% CI 0.78, 0.99) and specificity of 88% (95% CI 0.58, 0.98). Based on intensity of contrast medium uptake, 97 meningiomas could be diagnosed with intense uptake (93.75%). Sensitivity was lowest with 74% for meningiomas < 0.5 cm³, high with 95% for meningiomas > 2cm³ and highest with 100% for meningiomas 0.5-1.0 cm³. Petroclival meningiomas showed lowest sensitivity with 88% compared to sphenoidal meningiomas with 94% and orbital meningiomas with 100%. Specificity of meningioma diagnostic with MRI was high with 100% for sphenoidal and hemispherical-dural meningiomas and meningiomas with 0.5-1.0 and 1.0-2.0 cm³. Overall MRI enables reliable detection of meningiomas compared to PET/MRI. PET/MRI imaging offers highest sensitivity and specificity for small or difficult located meningiomas.

Detection of Isocitrate Dehydrogenase Mutated Glioblastomas Through Anomaly Detection Analytics

BACKGROUND

The rarity of Isocitrate Dehydrogenase mutated (mIDH) glioblastomas relative to wild-type IDH glioblastomas, as well as their distinct tumor physiology, effectively render them "outliers". Specialized tools are needed to identify these outliers.

OBJECTIVE

To carefully craft and apply anomaly detection methods to identify mIDH glioblastoma based on radiomic features derived from magnetic resonance imaging.

METHODS

T1-post gadolinium images for 188 patients and 138 patients were downloaded from The Cancer Imaging Archive's (TCIA) The Cancer Genome Atlas (TCGA) glioblastoma collection, and from the University of Minnesota Medical Center (UMMC), respectively. Anomaly detection methods were tested on glioblastoma image features for the precision of mIDH detection and compared to standard classification methods.

RESULTS

Using anomaly detection training methods, we were able to detect IDH mutations from features in noncontrast-enhancing regions in glioblastoma with an average precision of 75.0%, 69.9%, and 69.8% using three different models. Anomaly detection methods consistently outperformed traditional two-class classification methods from 2 unique learning models (67.9%, 67.6%). The disparity in performances could not be overcome through newer, popular models such as neural networks (67.4%).

CONCLUSION

We employed an anomaly detection strategy in the detection of IDH mutation in glioblastoma using preoperative T1 postcontrast imaging. We show these methods outperform traditional two-class classification in the setting of dataset imbalances inherent to IDH mutation prevalence in glioblastoma. We validate our results using an external dataset and highlight new possible avenues for radiogenomic rare event prediction in glioblastoma and beyond.

Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9-92.5% CA, 80-95% sensitivity and 80-90% specificity. AUC scores in the range of 0.82-0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

Maximal tumor diameter in the preoperative tumor magnetic resonance imaging (MRI) T2 image is associated with prognosis of Grade II Glioma

Factors associated with the prognosis of low-grade glioma remain undefined. In this study, we examined whether the maximal tumor diameter in the preoperative tumor magnetic resonance imaging (MRI) T2 image is associated with the prognosis of grade II gliomas patients, aiming to provide insights into the clinical prediction of patient outcome.We retrospectively analyzed the clinical data of patients with Grade II glioma, who were hospitalized in Xiangya Hospital, Central South University, from 2011 to 2016. Kaplan-Meier and Cox proportional hazards analyses were performed to determine the association between maximal tumor diameter and prognosis.A total of 90 patients with grade II glioma were included in this study. Mean patient age was 37.7 ± 13.0 years, and 58.9% of them were male. Kaplan-Meier survival analysis of overall survival (overall survival [OS], P = .009) and event-free survival (EFS, P = .002) revealed statistically significant differences between the patients with lesion diameter <7 cm and those with lesion diameter ≥7 cm. The maximal tumor diameter in the preoperative tumor MRI T2 image was identified as a prognostic factor of OS (P = .013), while constituting an independent risk factor for EFS (P = .002) alongside elevated histological grade after recurrence (P = .006).The maximal tumor diameter in the preoperative tumor MRI T2 image independently predicts OS and EFS in patients with grade II glioma.

Epidemiology of Anthropometric Factors in Glioblastoma Multiforme-Literature Review

Although glioblastoma multiforme (GBM) is a widely researched cancer of the central nervous system, we still do not know its full pathophysiological mechanism and we still lack effective treatment methods as the current combination of surgery, radiotherapy, and chemotherapy does not bring about satisfactory results. The median survival time for GBM patients is only about 15 months. In this paper, we present the epidemiology of central nervous system (CNS) tumors and review the epidemiological data on GBM regarding gender, age, weight, height, and tumor location. The data indicate the possible influence of some anthropometric factors on the occurrence of GBM, especially in those who are male, elderly, overweight, and/or are taller. However, this review of single and small-size epidemiological studies should not be treated as definitive due to differences in the survey methods used. Detailed epidemiological registers could help identify the main at-risk groups which could then be used as homogenous study groups in research worldwide. Such research, with less distortion from various factors, could help identify the pathomechanisms that lead to the development of GBM.

Magnetic resonance imaging of the hypothalamo-pituitary region

The diagnosis and management of mass lesions in the sellar and parasellar areas remain challenging. When approaching patients with possible sellar or hypothalamic masses, it is important not only to focus on imaging but also detect possible pituitary hormone deficits or excess, in order to establish an appropriate diagnosis and initiate treatment. The imaging modalities used to characterize hypothalamic and pituitary lesions have significantly evolved over the course of the past several years. Computed tomography (CT) and CT angiography play a major role in detecting various sellar lesions, especially in patients who have contraindications to magnetic resonance imaging (MRI) and can also yield important information for surgical planning. However, MRI has become the gold standard for the detection and characterization of hypothalamic and pituitary tumors, infections, cystic, or vascular lesions. Indeed, the imaging characteristics of hypothalamic and sellar lesions can help narrow down the differential diagnosis preoperatively. In addition, MRI can help establish the relationship of mass lesions to surrounding structures. A pituitary MRI examination should be obtained if there is concern for mass effect (including visual loss, ophthalmoplegia, headache) or if there is clinical suspicion and laboratory evidence of either hypopituitarism or pituitary hormone excess. The information obtained from MRI images also provides us with assistance in planning surgery. Using intraoperative MRI can be very helpful in assessing the adequacy of tumor resection. In addition, MRI images yield reliable data that allow for noninvasive monitoring of patients postoperatively.

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma

Purpose

There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network “Glioma MR Imaging 2.0” (GliMR) which we present in this article.

Methods

GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current “state-of-the-art” in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019.

Results

GliMR’s first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website.

Conclusion

The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.

Arterial spin labeling in the grading of brain gliomas: could it help?

Background

Gliomas are characterized by high morbidity and mortality with low cure and high recurrence rates, which depends to a great degree on the angiogenesis of the tumor. Assessment of such angiogenesis by perfusion techniques is of utmost importance for the preoperative grading of gliomas. The purpose of this study was to assess the role of arterial spin labeling (ASL) perfusion as a non-contrast MRI technique in the grading of brain gliomas, in correlation with the dynamic susceptibility contrast perfusion imaging (DSC-PI). The study was carried out on 35 patients admitted to the Neurosurgery Department with MRI features of gliomas and sent for further perfusion imaging. Non-contrast ASL followed by DSC-PI was done for all cases. The final diagnosis of the cases was established by histopathology.

Results

Fourteen patients (14/35) had low-grade gliomas while twenty-one (21/35) had high-grade gliomas. In low-grade gliomas, four cases out of 14 were falsely graded as high-grade tumors showing hyperperfusion on ASL, three of which showed DSC-PI hypoperfusion. In high-grade gliomas, two cases out of 21 were interpreted as an indeterminate grade by ASL showing isoperfusion, however showed hyperperfusion on DSC-PI. ROC curve analysis showed ASL-derived rCBF > 2.08 to have 80.95% sensitivity, 85.71% specificity, and overall accuracy of 82.86% compared to 100% sensitivity, specificity, and accuracy of DSC-PI-derived rCBV and rCBF of > 1.1 and > 0.9, respectively. A significant positive correlation was noted between ASL and DSC-PI with correlation coefficient reaching r = 0.80 between ASL-rCBF and DSC-rCBF (p < 0.01) and r = 0.68 between ASL and DSC-rCBV (p < 0.01).

Conclusions

ASL is a relatively recent non-contrast perfusion technique that obtains results which are in fair agreement with the more established DSC perfusion imaging making it an alternative method for preoperative assessment of perfusion of gliomas, especially for patients with contraindications to contrast agents.

Towards effective machine learning in medical imaging analysis: A novel approach and expert evaluation of high-grade glioma 'ground truth' simulation on MRI

PURPOSE/OBJECTIVE(S)

Gliomas are uniformly fatal brain tumours with significant neurological and quality of life detriment to patients. Improvement in outcomes has remained largely unchanged in nearly 20 years. MRI (magnetic resonance imaging) is often used in diagnosis and management. Machine learning analyses of large-scale MRI data are pivotal in advancing the diagnosis, management and improve outcomes in neuro-oncology. A common challenge to robust machine learning approaches is the lack of large 'ground truth' datasets in supervised learning for building classification and prediction models. The creation of these datasets relies on human-expert input and is time-consuming and subjective error-prone, limiting effective machine learning applications. Simulation of mechanistic aspects such as geometry, location and physical properties of brain tumours can generate large-scale ground-truth datasets allowing for comparison of analysis techniques in clinical applications. We aimed to develop a transparent and convenient method for building 'ground truth' presentations of simulated glioma lesions on anatomical MRI.

MATERIALS/METHODS

The simulation workflow was created using the Feature Manipulation Engine (FME®), a data integration platform specializing in the spatial data processing. By compiling and integrating FME's functions to read, integrate, transform, validate, save, and display MRI data, and experimenting with ways to manipulate the parameters concerning location, size, shape, and signal intensity with the presentations of glioma, we were able to generate simulated appearances of high-grade gliomas on gadolinium-based high-resolution 3D T1-weighted MRI (1 mm³). Data of patients with canonical high-grade tumours were used as real-world tumours for validating the accuracy of the simulation. Twenty raters who are experienced with brain tumour interpretation on MRI independently completed a survey, designed to distinguish simulated and real-world brain tumours. Sensitivity and specificity were calculated for assessing the performance of the approach with the binary classification of simulated vs real-world tumours. Correlation and regression were used in run time analysis, assessing the software toolset's efficiency in producing different numbers of simulated lesions. Differences in the group means were examined using the non-parametric Kruskal-Wallis test.

RESULTS

The simulation method was developed as an interpretable and useful workflow for the easy creation of tumour simulations and incorporation into 3D MRI. A linear increase in the running time and memory usage was observed with an increasing number of generated lesions. The respondents' accuracy rate ranged between 33.3 and 83.3 %. The sensitivity and specificity were low for a human expert to differentiate simulated lesions from real gliomas (0.43 and 0.58) or vice versa (0.65 and 0.62). The mean scores ranking the real-world gliomas did not differ between the simulated and real tumours.

CONCLUSION

The reliable and user-friendly software method can allow for robust simulation of high-grade glioma on MRI. Ongoing research efforts include optimizing the workflow for generating glioma datasets as well as adapting it to simulating additional MRI brain changes.

Clinical, radiological and molecular characterization of intramedullary astrocytomas

Intramedullary astrocytomas (IMAs) are rare tumors, and few studies specific to the molecular alterations of IMAs have been performed. Recently, KIAA1549-BRAF fusions and the H3F3A p.K27M mutation have been described in low-grade (LG) and high-grade (HG) IMAs, respectively. In the present study, we collected clinico-radiological data and performed targeted next-generation sequencing for 61 IMAs (26 grade I pilocytic, 17 grade II diffuse, 3 LG, 3 grade III and 12 grade IV) to identify KIAA1549-BRAF fusions and mutations in 33 genes commonly implicated in gliomas and the 1p/19q regions. One hundred seventeen brain astrocytomas were analyzed for comparison. While we did not observe a difference in clinico-radiological features between LG and HG IMAs, we observed significantly different overall survival (OS) and event-free survival (EFS). Multivariate analysis showed that the tumor grade was associated with better OS while EFS was strongly impacted by tumor grade and surgery, with higher rates of disease progression in cases in which only biopsy could be performed. For LG IMAs, EFS was only impacted by surgery and not by grade. The most common mutations found in IMAs involved TP53, H3F3A p.K27M and ATRX. As in the brain, grade I pilocytic IMAs frequently harbored KIAA1549-BRAF fusions but with different fusion types. Non-canonical IDH mutations were observed in only 2 grade II diffuse IMAs. No EGFR or TERT promoter alterations were found in IDH wild-type grade II diffuse IMAs. These latter tumors seem to have a good prognosis, and only 2 cases underwent anaplastic evolution. All of the HG IMAs presented at least one molecular alteration, with the most frequent one being the H3F3A p.K27M mutation. The H3F3A p.K27M mutation showed significant associations with OS and EFS after multivariate analysis. This study emphasizes that IMAs have distinct clinico-radiological, natural evolution and molecular landscapes from brain astrocytomas.

Molecular and Cellular Complexity of Glioma. Focus on Tumour Microenvironment and the Use of Molecular and Imaging Biomarkers to Overcome Treatment Resistance

This review highlights the importance and the complexity of tumour biology and microenvironment in the progression and therapy resistance of glioma. Specific gene mutations, the possible functions of several non-coding microRNAs and the intra-tumour and inter-tumour heterogeneity of cell types contribute to limit the efficacy of the actual therapeutic options. In this scenario, identification of molecular biomarkers of response and the use of multimodal in vivo imaging and in particular the Positron Emission Tomography (PET) based molecular approach, can help identifying glioma features and the modifications occurring during therapy at a regional level. Indeed, a better understanding of tumor heterogeneity and the development of diagnostic procedures can favor the identification of a cluster of patients for personalized medicine in order to improve the survival and their quality of life.

The Role of RNA and DNA Aptamers in Glioblastoma Diagnosis and Therapy: A Systematic Review of the Literature

Glioblastoma (GBM) is the most lethal primary brain tumor of the central nervous system in adults. Despite advances in surgical and medical neuro-oncology, the median survival is about 15 months. For this reason, initial diagnosis, prognosis, and targeted therapy of GBM represent very attractive areas of study. Aptamers are short three-dimensional structures of single-stranded nucleic acids (RNA or DNA), identified by an in vitro process, named systematic evolution of ligands by exponential enrichment (SELEX), starting from a partially random oligonucleotide library. They bind to a molecular target with high affinity and specificity and can be easily modified to optimize binding affinity and selectivity. Thanks to their properties (low immunogenicity and toxicity, long stability, and low production variability), a large number of aptamers have been selected against GBM biomarkers and provide specific imaging agents and therapeutics to improve the diagnosis and treatment of GBM. However, the use of aptamers in GBM diagnosis and treatment still represents an underdeveloped topic, mainly due to limited literature in the research world. On these bases, we performed a systematic review aimed at summarizing current knowledge on the new promising DNA and RNA aptamer-based molecules for GBM diagnosis and treatment. Thirty-eight studies from 2000 were included and investigated. Seventeen involved the use of aptamers for GBM diagnosis and 21 for GBM therapy. Our findings showed that a number of DNA and RNA aptamers are promising diagnostic and therapeutic tools for GBM management.

Radiomics in radiation oncology-basics, methods, and limitations

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

Stratifying Brain Tumour Histological Sub-Types: The Application of ATR-FTIR Serum Spectroscopy in Secondary Care

Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies >80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients.

The MRI Features and Prognosis of Gliomas Associated With IDH1 Mutation: A Single Center Study in Southwest China

Purpose: To investigate the associations of MRI radiological features and prognosis of glioma with the status of isocitrate dehydrogenase 1 (IDH1).

Material and Methods: A total of 116 patients with gliomas were retrospectively recruited from January 2013 to December 2015. All patients were undergone routine MRI (T1WI, T2WI, T2-FLAIR) scanning and contrast-enhanced MRI T1WI before surgery. The following imaging features were included: tumor location, diameter, the pattern of growth, boundary, the degree of enhancement, mass effect, edema, cross the middle line, under the ependyma. χ² and Fisher's exact probability tests were used to determine the significance of associations between MRI features and IDH1 mutation of glioma. The survival distributions were estimated using Kaplan-Meier compared by Log-rank test. Univariate and multivariate analyses were performed using Cox regression.

Results: Gliomas with IDH1 mutant were significantly more likely to exhibit homogeneous signal intensity (p = 0.009) on non-contrast MRI protocols and less contrast enhancement (p = 0.000) on contrast enhanced T1WI. IDH1 mutant type glioma was more inclined to cross the midline to invade contralateral hemisphere (p = 0.001). The overall survival between IDH1 mutated and wild type glioma were significantly different (p = 0.000), age ≤ 40 (p = 0.003), KPS scores > 80 before operation (p = 0.000) and low grade glioma (p = 0.000).

Conclusions: Our results suggest IDH1 mutant in gliomas is more likely to exhibit homogeneous signal intensity, less contrast enhancement and more inclined to cross the midline. Patients with IDH1 mutated, age ≤ 40, KPS scores > 80 before operation and low-grade glioma may have a longer life and better prognosis.

Investigation of promoter methylation of MCPH1 gene in circulating cell-free DNA of brain tumor patients

INTRODUCTION

Despite advanced diagnostic and therapeutic techniques, many brain tumors are still diagnosed at high grades and, therefore finding novel molecular markers may assist in early detection and reducing brain tumors-related mortality rate. Owing to the previous reports on the importance of MCPH1 gene in tumorigenesis, the present study was aimed to study the promoter methylation of MCPH1 gene in paired circulating cell-free DNA (cfDNA) and tumor tissues of brain tumor patients.

MATERIALS AND METHODS

Fourteen fresh paired serum and tumor tissue samples in addition to 18 isolated serum samples were collected from patients affected by different grades of brain tumor. Genomic DNA and cfDNA was isolated from tissue and serum samples using QIAamp DNA Mini Kit Norgen Bioteck Kit, respectively. Methylation DNA immunoprecipitation Real-time polymerization chain reaction (MeDIP-Real-time PCR) was performed on isolated DNA samples using EpiQuik MeDIP Ultra Kit and specific primer pairs. cfDNA quantity was determined through Real-time PCR analysis using specific primer pairs designed for GAPDH gene.

RESULTS

MCPH1 was methylated in 54% of cfDNA samples which was significantly associated with tumor grade, as well (P-value = 0.02). The methylation rate of MCPH1 was found as 78% in the tissue samples which was meaningfully associated with tumor grade (P-value = 0.03). Moreover, methylation of the MCPH1 gene was consistent in 57% of the same cfDNA and tissue samples. Methylation of MCPH1 gene in neither tumor tissues nor cfDNA was not correlated with age and sex of the patients.

DISCUSSION AND CONCLUSION

Due to the conformity of methylation of MCPH1 gene in cfDNA and tissue samples in more than half of the enrolled patients, especially in higher grades of tumors, it seems that MCPH1 promoter methylation could be a potential epimarker in not only detection of brain tumors but also in response to chemo- and radiotherapy which warranted further assessment.

[Current Applications and Future Perspectives of Brain Tumor Imaging]

뇌종양의 진단 및 치료 반응 평가의 기본이 되는 영상기법은 해부학적 영상이다. 현재 임상에서 사용 가능한 영상기법들 중 확산 강조 영상 및 관류 영상이 추가적인 정보를 제공하고 있다. 최근에는 종양의 유전체 변이와 이질성 평가가 중요해지면서 라디오믹스와 딥러닝을 이용한 영상분석기법의 임상 응용이 기대되고 있다. 본 종설에서는 뇌종양 영상 임상 적용에서 여전히 중요한 해부학적 영상을 중심으로 한 자기공명영상 촬영 권고안, 최신 영상기법 중 확산 강조 영상 및 관류 영상의 기본 원리, 병태생리학적 배경 및 임상응용, 마지막으로 최근 컴퓨터 기술의 발전으로 많이 연구되고 있는 라디오믹스와 딥러닝의 뇌종양에서의 향후 활용가치에 대해 기술하고자 한다.

Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma

Glioblastoma is the most common and most malignant primary brain tumor. Despite aggressive multimodal treatment, its prognosis remains poor. Even with continuous developments in MRI, which has provided us with newer insights into the diagnosis and understanding of tumor biology, response assessment in the posttherapy setting remains challenging. We believe that the integration of additional information from advanced neuroimaging techniques can further improve the diagnostic accuracy of conventional MRI. In this article, we review the utility of advanced neuroimaging techniques such as diffusion-weighted imaging, diffusion tensor imaging, perfusion-weighted imaging, proton magnetic resonance spectroscopy, and chemical exchange saturation transfer in characterizing and evaluating treatment response in patients with glioblastoma. We will also discuss the existing challenges and limitations of using these techniques in clinical settings and possible solutions to avoiding pitfalls in study design, data acquisition, and analysis for future studies. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:978-997.